• • Different types of data
• Data summarization
• Frequency table
• Frequency Distributions
• Histogram
• Measures of central tendency and dispersion
• Skewness and kurtosis
• Basic Probability, Conditional Probability
• Normal Distribution
• Sampling methods
• Point and Interval estimation
• Central Limit Theorem
• Null and alternative hypothesis
• Level of significance
• P value
• Types of errors
• Hypothesis Testing

Linear and Multiple Linear Regression

• Simple and Multiple Linear Regression
• ANOVA
• Interpretation of coefficients
• Dummy Variables
• Residual Analysis
• Outliers

Logistic Regression

• Assumptions
• Logistic Function
• Chi-Square
• Hosmer Lemeshow test
• Kolmogorov-Smirnov statistic and chart
• Classification Table
• Interpreting Coefficients
• Dependent Variable Prediction
• Principles of Forecasting
• Time Series
• Causal models
• Types of Forecasting Methods and their characteristics
• Moving Average
• Exponential Smoothing
• Trend
• Seasonality
• Cyclicity
• ARIMA

Classification

• Decision Tree Induction
• Bayes Methods
• Rule-Based Classification
• Model Evaluation and Selection
• Ensemble Approaches
• Random forest

Clustering

• Partitioning Methods
• Hierarchical Methods
• Density-Based Methods
• Grid-Based Methods
• Evaluation of Clustering
• K-means Method

Excel

• Formatting of Excel Sheets
• Use of Excel Formula Function
• Data Filter and Sort
• Charts and Graphs
• Table formula and Scenario building
• Lookups
• Pivot tables

Python & R

• Data types
• Important Packages
• Data Manipulation
• Building models using learned algorithms
• Evaluating and optimizing models

Tableau – (Data Visualization tool)

• Extracting data into Tableau
• Data Preparation, Dimensions
• Transformation of variables
• Creating Views
• Working with charts
• Exporting visualisations

SQL – Introduction to Databases

• Terminologies – Records, Fields, Tables
• Introduction to database
• Introduction to SQL
• SQL Syntax
• SQL data Types
• SQL Operators
• Table creation in SQL- Create, Insert, Drop, Delete and Update
• Table access & Manipulation
• Select with Where Clause (In between, logical operators, wild cards, order, group by)
• Concepts of Join – Inner, Outer
• Projects let you apply what you’ve learned in Business Analytics course to a practical problem. In project, you will be given a problem statement and the relevant data. You will apply various algorithm techniques to find an optimum solution to the problem.
• Once you’ve completed the project, you’ll be better able to apply analytical techniques to a business case and accordingly prepare a detailed report.
• In case you don’t have any relevant experience in Analytics, this project will enable you to showcase your expertise in a job interview.
•  Property Price Prediction using Linear Regression
• Bank Loan Prediction using Logistic Regression
• Wine buyer categorization using Clustering
• Forecasting Demand using Time Series
•  Human Activity Recognition using Random Forest
•  Predicting Potential Buyer using Decision Tree